Abstract
Sentinel-2 (S2) multi-spectral instrument (MSI) images are used in an automated approach built on fuzzy set theory and a region growing (RG) algorithm to identify areas affected by fires in Mediterranean regions. S2 spectral bands and their post- and pre-fire date (Δpost-pre) difference are interpreted as evidence of burn through soft constraints of membership functions defined from statistics of burned/unburned training regions; evidence of burn brought by the S2 spectral bands (partial evidence) is integrated using ordered weighted averaging (OWA) operators that provide synthetic score layers of likelihood of burn (global evidence of burn) that are combined in an RG algorithm. The algorithm is defined over a training site located in Italy, Vesuvius National Park, where membership functions are defined and OWA and RG algorithms are first tested. Over this site, validation is carried out by comparison with reference fire perimeters derived from supervised classification of very high-resolution (VHR) PlanetScope images leading to more than satisfactory results with Dice coefficient > 0.84, commission error < 0.22 and omission error < 0.15. The algorithm is tested for exportability over five sites in Portugal (1), Spain (2) and Greece (2) to evaluate the performance by comparison with fire reference perimeters derived from the Copernicus Emergency Management Service (EMS) database. In these sites, we estimate commission error < 0.15, omission error < 0.1 and Dice coefficient > 0.9 with accuracy in some cases greater than values obtained in the training site. Regression analysis confirmed the satisfactory accuracy levels achieved over all sites. The algorithm proposed offers the advantages of being least dependent on a priori/supervised selection for input bands (by building on the integration of redundant partial burn evidence) and for criteria/threshold to obtain segmentation into burned/unburned areas.
Highlights
Wildfires are the largest contributor to global biomass burning (BB) and represent a significant dynamic component of ecosystems, affecting terrestrial and atmosphere systems [1,2]
In the Calar site, the OWAOR growing layer generates a significantly greater commission error by mistakenly classifying as burned a region of woodland–shrubland located in the northeastern part of the site
In the Zakynthos site, Greece, commission error for the OR-like ordered weighted averaging (OWA) operators is greater than OWAAverage and mainly located in sparsely vegetated land covers
Summary
Wildfires are the largest contributor to global biomass burning (BB) and represent a significant dynamic component of ecosystems, affecting terrestrial and atmosphere systems [1,2]. Fires impact on atmospheric chemistry, with aerosols and greenhouse gas emissions [6], the carbon budgets [7], hydrological cycles, soils and vegetation components of ecosystems [8,9]. In this framework, the extent of the area affected by fires is critical to Remote Sens. Coarse-resolution RS data have been proved to be the most suitable source for depicting fire distribution over large areas and one primary image source for burned area (BA) products is the Moderate Resolution Imaging Spectrometer (MODIS) [13]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
